Many digital devices using video cameras and digital cameras have been developed to count the number of objects in an image. These cameras, however, must take one image including the objects. An example is the device counting the number of cells in an image taken by a microscope. Since chromosome of the cells are dyed purple, the device can count the number of the cells, segmenting purple areas which are bigger than the regular size from the image and making them a lump. In any cases that some troubles happen in dying chromosome purple and chromosome can not be dyed purple, however, it is difficult for this device to count the number of the cells, because the cells are transparent in general and color information is no good for making the whole cell a lump. Even though the device can catch a core and some Mitochondrias of an expanding cell image, this way can not always be used, and many devices calcurate edge information in terms of contour of the cells appeared by refraction and reflection of light. Although the edge information should theoretically make the whole cell a lump, the devices complement the edge information by the form and size of the cell since the edge information is often incomplete in fact. The devices also must perform heavy image processing-like painting out in order to make the whole cell a lump, using the edge information, where the edge information must be continuous. In addition, since the devices must calcurate optical flow and so on, in order to select moving cells from all cells, the devices are too expensive to calcurate information exactly. Otherwise, the cheap devices lose much time.
Generally speaking, now, objects have certain colors except some objects like cells. For example, back of a tadpole is roughly dark brown, an apple is almost red, yellow and green, a horse is almost black, brown and gray, and a crow is almost black. However, even though the devices find peculiar color information of objects from an image in order to count the number of these objects, it is very difficult for the devices to count the number, because the color information depends on brightness of solor light and lighting, and performance of a camera. Another reason is the difficulty of classifying objects as targets or others when the objects are similar color in taking a image. A general way is to count the number of the objects after segmenting the targets from background by using information of their form and size, where color information is used to select the targets and to reduce computational complexity. If a visual device regards variance of color information as movement of a object, lighting and camera performance seldom comes to be a problem, but it becomes more difficult for the device to reproduce form of the object exactly. Moreover, painting out a domain surrounded for edge information makes a problem that the device must decide an object area. The visual device using variance of color information, then, has not been researched in details.
Considering these facts, a visual device comes to count the number of objects in spite of their feature and the environment taking an image, when it regards information like variance of color information as movement of the objects, generates edge information from the movement, selects the objects using the edge information, and makes some object areas a lump. In addition, we can expect that the visual device counts even the number of still objects if it can generate edge information from color information in such cases that the objects are vibrated, a camera is vibrated, or a taken image is vibrated.
Suppose now that there is the previous visual device possible to count the number of objects taken by a camera. If the objects are still, the visual device can always count their number. If they are moving, however, it can only count their number while they are taken by the camera there is no problem if all positions of still and moving objects are specified beforehand, as cells in a laboratory dish. In a case, however, that the visual device counts humans and animals walking in a room and along outdoors, it can be used for only limited targets if the camera is fixed, because the camera can not catch the whole room and the whole outdoor area, and an image of the humans and the animals becomes big or small, corresponding to distance from the camera. Moreover, heavy computational complexity is desired for the visual device to recognize the objects since it must distinguish the humans and the animals from interior objects of the room.
Considering these facts, the visual device comes not only to recognize such objects as the humans and the animals easily, but also to count the number of moving objects like them, whose position can not be specified beforehand, if a moving camera can find out the objects in the room and along the outdoors, and take only an image appearing them, and if the visual device can adjust the magnification of the camera as their size in the image is suitable for it. Of course, we can expect that the visual device can count the number of objects like the humans and the animals distinguished from other still objects even though they seldom move in sleeping.
In the present invention, the visual device counts the number of moving objects and all objects at high speed, based on edge information generated from either the moving objects or all of the objects, which are selected by it, in an animation image. The visual device also calcurate the rate of the moving objects and still objects in the image at hight speed, by counting the number of these objects. In addition, the visual device counts the number of the moving objects and the still objects at high speed, by finding out them possible to be taken by a moving camera.